Abstract
According to the requirements of point cloud simplification for T-profile steel plate welding in shipbuilding, the disadvantages of the existing simplification algorithms are analyzed. In this paper, a point cloud simplification method is proposed based on octree coding and the threshold of the surface curvature feature. In this method, the original point cloud data are divided into multiple sub-cubes with specified side lengths by octree coding, and the points that are closest to the gravity center of the sub-cube are kept. The k-neighborhood method and the curvature calculation are performed in order to obtain the curvature features of the point cloud. Additionally, the point cloud data are divided into several regions based on the given adjustable curvature threshold. Finally, combining the random sampling method with the simplification method based on the regional gravity center, the T-profile point cloud data can be simplified. In this study, after obtaining the point cloud data of a T-profile plate, the proposed simplification method is compared with some other simplification methods. It is found that the proposed simplification method for the point cloud of the T-profile steel plate for shipbuilding is faster than the three existing simplification methods, while retaining more feature points and having approximately the same reduction rates.
Highlights
At present, the welding of T-profile steel plates are still completed manually in ship plants in China
Considering the demand for automated T-profile steel plate welding in shipbuilding, a point cloud simplification algorithm is presented in this paper focused on the simplicity, efficiency and accuracy requirements of point cloud reduction
The welding zone feature point retention rate is defined as the ratio of the amount of point cloud data remaining in the weld zone to the total point cloud data volume in the weld zone
Summary
The welding of T-profile steel plates (shown in Figure 1) are still completed manually in ship plants in China. This is costly and labor intensive with low efficiency. Reducing the data as much as possible is needed with the consideration of keeping an acceptable level of accuracy. In this process, the redundant data points and the noise of them should be removed at an acceptable processing speed [7]
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